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Generally, adaptive bitrates for variable Internet bandwidths can be obtainedthrough multi-pass coding. Referenceless prediction-based methods showpractical benefits compared with multi-pass coding to avoid excessivecomputational resource consumption, especially in low-latency circumstances.However, most of them fail to predict precisely due to the complex innerstructure of modern codecs. Therefore, to improve the fidelity of prediction,we propose a referenceless prediction-based R-QP modeling (PmR-QP) method toestimate bitrate by leveraging a deep learning algorithm with only one-passcoding. It refines the global rate-control paradigm in modern codecs onflexibility and applicability with few adjustments as possible. By exploringthe potentials of bitstream and pixel features from the prerequisite ofone-pass coding, it can reach the expectation of bitrate estimation in terms ofprecision. To be more specific, we first describe the R-QP relationship curveas a robust quadratic R-QP modeling function derived from the Cauchy-baseddistribution. Second, we simplify the modeling function by fastening oneoperational point of the relationship curve received from the coding process.Third, we learn the model parameters from bitstream and pixel features, namedthem hybrid referenceless features, comprising texture information,hierarchical coding structure, and selected modes in intra-prediction.Extensive experiments demonstrate the proposed method significantly decreasesthe proportion of samples' bitrate estimation error within 10% by 24.60% onaverage over the state-of-the-art.

Referenceless Rate-Distortion Modeling with Learning from Bitstream and Pixel Features